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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

ADVANCED-STEM-BASED DEEP LEARNING FOR SEMANTIC SEGMENTATION OF DEFECTS IN STEELS

In this work, we developed a novel deep convolutional neural network (DCNN) model, called DefectNet, for robust and automated semantic segmentation of three crystallographic defects including line dislocations, precipitates and voids commonly observed in structural metals and alloys. In previous work, we established an experimental protocol for a diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) technique and tailored it specifically for imaging defects in popular iron-based structural alloy. Thus, the DefectNet was trained over a small but high-quality DCI STEM defect image sets of a HT-9 martensitic steel before and after neutron irradiation for 111.8 dpa at 412°C. For the defect quantification that typically takes at least half an hour even for an expert, with a good model the machine learning algorithm can produce more reproducible and reliable quantification in a few seconds. This work demonstrates the feasibility of using deep learning algorithms for fast and accurate defect quantitative analysis.

Machine Learning, Radiation effects, Image analysi↗
NASA NTRS1992

An application of synthetic seismicity in earthquake statistics - The Middle America Trench

The way in which seismicity calculations which are based on the concept of fault segmentation incorporate the physics of faulting through static dislocation theory can improve earthquake recurrence statistics and hone the probabilities of hazard is shown. For the Middle America Trench, the spread parameters of the best-fitting lognormal or Weibull distributions (about 0.75) are much larger than the 0.21 intrinsic spread proposed in the Nishenko Buland (1987) hypothesis. Stress interaction between fault segments disrupts time or slip predictability and causes earthquake recurrence to be far more aperiodic than has been suggested.

Ward, Steven N.↗
DOE OSTI2019

DefectSegNet

DefectSegNet for semantic segmentation of defects in Steels The first and most important step of automating defect analysis is perceptual defect identification. In terms of digital image processing, semantic segmentation best emulates human recognition of defect features – it identifies the crystallographic defects and where they are located in a TEM micrograph. We solved this problem by developing DefectSegNet, a novel hybrid CNN algorithm for robust and automated semantic segmentation of three crystallographic defects, including line dislocations, precipitates and voids, that are commonly observed in structural metals and alloys.

Sainju, Rajat↗
DOE OSTI2019

Deep Learning for Semantic Segmentation of Defects in Advanced STEM Images of Steels

Crystalline materials exhibit long-range ordered lattice unit, within which resides nonperiodic structural features called defects. These crystallographic defects play a vital role in determining the physical and mechanical properties of a wide range of material systems. While computer vision has demonstrated success in recognizing feature patterns in images with well defined contrast, automated identification of nanometer scale crystallographic defects in electron micrographs governed by complex contrast mechanism is still a challenging task. Here, building upon an advanced defect imaging mode that offers high feature clarity, we introduce DefectSegNet - a new convolutional neural network (CNN) architecture that performs semantic segmentation of three common crystallographic defects in structural alloys: dislocation lines, precipitates and voids. Results from supervised training of a small set of high-quality defect images of steels show high pixel-wise accuracy across all three types of defects: 91.60 ± 1.77% on dislocations, 93.39 ± 1.00% on precipitates, and 98.85 ± 0.56% on voids. We discuss the sources of uncertainties in CNN prediction, and the training data in terms of feature density, representation and homogeneity and their effects on deep learning performance. Further defect quantification using DefectSegNet prediction outperforms human expert average, presenting a promising new workflow for fast and statistically meaningful quantification of materials defects.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Simulation of the interaction between an edge dislocation and $\langle$111$\rangle$ interstitial dislocation loops in α-iron

The dependence of the interactions of intermediate-size ½<111> self-interstitial atom (SIA) loops with an edge dislocation on strain rate and temperature was discovered by molecular dynamics (MD) simulations for the interatomic potential derived by Ackland et al. (A97). For low temperatures ($T$ =1 K), the mechanisms of the interactions were in agreement with recent literature. It was demonstrated that a second passing of the dislocation through the loop led to a different mechanism than the one that occurred upon first passing. Since these mechanisms are associated with different SIA loop sizes, and since the loop lost a number of SIAs upon first interaction, it was deduced that the dividing threshold between large and small loops (rendering them strong or weak obstacles, respectively) is at the vicinity of the loop size studied (169 SIAs). For higher temperatures ($T$ = 300 K), the strain rate dependence proved strong: for low strain rates, the dislocation absorbed the loop as a double super-jog almost immediately and continued its glide unimpeded. For a high strain rate, the dislocation was initially pinned due to the formation of an almost sessile segment leading to high critical stress.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Fault geometry inversion and slip distribution of the 2010 M w 7.2 El Mayor-Cucapah earthquake from geodetic data

The 4 April 2010 M w 7.2 El Mayor-Cucapah (EMC) earthquake in Baja, California, and Sonora, Mexico, had primarily right-lateral strike-slip motion and a minor normal-slip component. The surface rupture extended about 120 km in a NW-SE direction, west of the Cerro Prieto fault. We use geodetic measurements including near- to far-field GPS, interferometric synthetic aperture radar (InSAR), and subpixel offset measurements of radar and optical images to characterize the fault slip during the EMC event. We use dislocation inversion methods and determine an optimal nine-segment fault geometry, as well as a subfault slip distribution from the geodetic measurements. With systematic perturbation of the fault dip angles, randomly removing one geodetic data constraint, or different data combinations, we are able to explore the robustness of the inferred slip distribution along fault strike and depth. The model fitting residuals imply contributions of early postseismic deformation to the InSAR measurements as well as lateral heterogeneity in the crustal elastic structure between the Peninsular Ranges and the Salton Trough. We also find that with incorporation of near-field geodetic data and finer fault patch size, the shallow slip deficit is reduced in the EMC event by reductions in the level of smoothing. Finally, these results show that the outcomes of coseismic inversions can vary greatly depending on model parameterization and methodology.

58 GEOSCIENCES↗
DOE OSTI2019

Sink strength and dislocation bias of three-dimensional microstructures

Irradiation induces significant changes in the microstructure of structural materials, many of which are driven by the preferential capture of point defects at particular sinks, such as dislocations. To quantify the kinetics of defect absorption at sinks, theoretical models of radiation damage generally rely on the concept of sink strength. However, analytical approaches to estimating the sink strength of dislocations rely, in turn, on a series of geometrical assumptions, idealizing the dislocation network as a series of infinite straight dislocations or isolated loops, often with artificial boundary conditions. In this paper, we use a recently developed technique to quantify point defect capture in three-dimensional dislocation networks. We integrate this technique with discrete dislocation dynamics to analyze the sink strengths of realistic dislocation microstructures consisting of a mixture of edge, screw, and junction segments, complete with an accurate description of the strain fields these microstructures produce and the resultant energetic interactions experienced by point defects. Furthermore, we show that the effective kinetics for absorbing point defects can vary significantly with the arrangement of the microstructure with a strong dependence on the structure and character of its dislocation content and introduce a surrogate model for sink strength which incorporates these effects.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Elastic interaction-induced anisotropic growth of dislocation loop arrays

The elastic interactions and reactions of dislocations lead to the formation of complex dislocation substructures, which is critical to the strain hardening and fatigue failure. Phase field dislocation dynamics simulations are conducted as a first step to understand the elastic interactions between dislocation loops. When the interloop spacing is small, the elastic interactions with neighboring loops become strong, rendering the edge segments strongly pinned, while allowing for the screw segments to propagate more easily. The interactions are found to result in an anisotropic stress distribution around the dislocation loops, leading to the formation of arrays of long, straight edge dislocations that could act as barriers to subsequent slip. Furthermore, the effect of initial loop size and applied strain rate on the elastic interaction-induced anisotropic pinning effect is investigated and discussed. Furthermore, the results are important for coarse-graining dislocation substructures formation into continuum level models of deformation in crystalline solids.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A coupled vacancy diffusion-dislocation dynamics model for the climb-glide motion of jogged screw dislocations

Here, we develop a novel model to study the climb/glide motion of jogged screw dislocations within the discrete dislocation dynamics (DDD) framework. We present results for the dependence of the climb velocity on the applied stress and on the jog size and distribution statistics. We show that the model predictions are consistent with experimental data in both γ-TiAl and Zircaloy-4. The ranges of the applied stress and jog spacing that determine the dominance of one of three dislocation mechanisms are identified. These are the jog dragging, dipole dragging, and dipole bypass mechanisms, respectively. The overall dislocation motion in the jog dragging regime is composed of glide of screw segments and climb of jogs, controlling the plastic strain and the creep rate, respectively. Based on current simulations and on a detailed examination of the predicted jog heights compared to experiments, we advance the hypothesis that a combination of jog dragging and dipole bypass mechanisms is necessary to reproduce the high creep rate observed in some experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Automated Quantitative Analysis of Extended Irradiation Defects - Dislocations, Voids and Precipitates in Neutron Irradiated HT-9 Steel

Structural alloys that make up the core components of nuclear reactors are exposed to radiation that can substantially alter the microstructure and mechanical properties of the material. Irradiation can cause the formation of extended defects including dislocation line and loops, voids and various types of precipitates. To understand the irradiation effects, it is critical to characterize these defects individually and collectively to quantify how they evolve throughout irradiation stages. Transmission electron microscopy (TEM) is a standard tool for providing extensive size, number density and spatial distribution of extended irradiation defects, however, manual measurement of defect size and number density requires time-intensive effort to analyze enough defects that provide a statistically meaningful result and are prone to errors of subjectivity. In this work, we developed a set of MATLAB algorithms offering robust and automated identification and quantification of extended irradiation defects like dislocation lines, voids and precipitates commonly observed in neutron irradiated cladding alloys [1]. Using a neutron-irradiated HT-9 martensitic alloy irradiated at 412°C to 111.8 dpa, we demonstrate the capability of these algorithms for quantitative defect analysis. Figure 1 presents defect images of the HT-9 using an advanced diffraction contrast imaging scanning transmission electron microscopy (DCI STEM) that suppresses bend contours and provides better defect contrast compared to conventional TEM [2]. The first step of defect analysis is defect identification, a process that can be digitally achieved by contrast thresholding (for simple defect contrast) and by machine learning computer vision for defect semantic segmentation [3]. Here, labeled binary defect images (Figures 2a and 2c) were used. For dislocation density quantification, we employed an optimized circular-grid intersection method first introduced by Dr. John Steeds [4]. This method is shown in Figures 2a and 2b, where the number of intersections of dislocation lines with circular traces was measured. This produces an unbiased sampling of dislocation lines, regardless of their orientation. This process was optimized with automated digital intersection number and grid length registration. Interestingly, it was found that a relatively large number of circles in a set circular gird is was needed to produce an invariant result (Figure 2b), with a low systematic measurement error. This demonstrates the main advantage of computer-based defect analysis, in performing repetitive operations quickly with a reduced systematic error. For the quantitative analysis of voids and precipitates a challenge encountered with automated defect analysis is feature overlap, particularly when the density of voids/precipitates is high. To mitigate this problem, as shown in Figure 2d, we modified the popular circular Hough transformation and treated ‘circular shape’ as the particle descriptor for voids/precipitates, efficiently resolving the overlapping particles. In summary, this automated digital quantification together with machine learning enabled defect identification [3] opens new opportunities for standardizing the characterization workflow for quantitative analysis of extended irradiation defects.

Sainju, Rajat↗
DOE OSTI2018

Quantification of complex-phase steel microstructure by using combined EBSD and EPMA measurements

Highlights: • In CP800 steel, bainite is the dominant phase with a typical lath (width 300 nm) structure and cementite precipitates. • Martensite is dispersed as small islands and only a few narrowed martensite bands are observed which has been induced by annealing process of cold rolled sheet. • The precise carbon content is measured in bainite and martensite utilizing EPMA line scans, which is around 0.2 wt% and 0.4 wt%, respectively. • Digitalization of the microstructure for CP800 is achieved by combining EBSD and EPMA techniques. • The final constituent map consists of plenty of microstructural features including topography, crystal structures, orientations and elemental distribution. - Abstract: We combined electron backscatter diffraction (EBSD) and electron probe microanalysis (EPMA) techniques to characterize the microstructure of industrial produced complex-phase CP800 steel. First, three major constituents (ferrite, martensite and bainite) are characterized. Then, transformation-induced dislocation zones in ferrite and bainitic regions consisting of different sub-structures are quantified and segmented. A final digitalized component map including plenty of microstructural features including crystal structures, orientations and elemental distribution are successfully achieved. The precise carbon content is measured in bainite and martensite utilizing EPMA line scans, which is around 0.2 wt% and 0.4 wt%, respectively. In the bainite phase of CP800, a typical Bagaryatski orientation relationship between cementite and lath-like bainitic ferrite is revealed based on the select diffraction patterns of transmission electron microscopy measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Microstructure of Neutron-Irradiated Al 3 Hf-Al Thermal Neutron Absorber Materials

A thermal neutron-absorbing metal matrix composite (MMC) comprised of Al 3 Hf particles in an aluminum matrix was developed to filter out thermal neutrons and create a fast flux environment for material testing in a mixed-spectrum nuclear reactor. Intermetallic Al 3 Hf particles capture thermal neutrons and are embedded in a highly conductive aluminum matrix that provides conductive cooling of the heat generated due to thermal neutron capture by the hafnium. These Al 3 Hf-Al MMCs were fabricated using powder metallurgy via hot pressing. The specimens were neutron-irradiated to between 1.12 and 5.38 dpa and temperatures ranging from 286 °C to 400 °C. The post-irradiation examination included microstructure characterization using transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy. This study reports the microstructural observations of four irradiated samples and one unirradiated control sample. All the samples showed the presence of oxide at the particle–matrix interface. The irradiated specimens revealed needle-like structures that extended from the surface of the Al 3 Hf particles into the Al matrix. An automated segmentation tool was implemented based on a YOLO11 computer vision-based approach to identify dislocation lines and loops in TEM images of the irradiated Al-Al 3 Hf MMCs. This work provides insight into the microstructural stability of Al 3 Hf-Al MMCs under irradiation, supporting their consideration as a novel neutron absorber that enables advanced spectral tailoring.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Generalizable Image Segmentation for Microstructure Characterization Through Integrated SEM and EBSD Analysis

We demonstrate generalizable semantic segmentation using minimal ground truth data. Correlated scanning electron microscopy (SEM) images and electron backscatter diffraction (EBSD) measurements of frictionstir processed 316L stainless steel plates were used to train deep learning models for grain boundary segmentation. Secondary electron (SE) imaging taken at an accelerating voltage of 10 keV correlated to EBSD-derived grain boundaries produced the best performing model. Notably, an ensemble of three models trained on a single SE image produced accurate segmentation over a series of BSE images of samples manufactured under different processing parameters, with a resultant mean absolute error in grain size of 0.34 µm. The striking generalizability of the models likely results from the similar escape depths of the SE training input and the EBSD training output and the reduced probability of dislocation artifacts appearing in the image. This finding highlights the importance of considering the physical principles behind imaging in the development of robust segmentation models for microstructure characterization.

Taufique, Mohammad Fuad Nur↗
DOE OSTI2016

Size effect, critical resolved shear stress, stacking fault energy, and solid solution strengthening in the CrMnFeCoNi high-entropy alloy

High-entropy alloys (HEAs) comprise a novel class of scientifically and technologically interesting materials. Among these, equatomic CrMnFeCoNi with the face-centered cubic (FCC) structure is noteworthy because its ductility and strength increase with decreasing temperature while maintaining outstanding fracture toughness at cryogenic temperatures. Here we report for the first time by single crystal micropillar compression that its bulk room temperature critical resolved shear stress (CRSS) is ~33–43MPa, ~10 times higher than that of pure nickel. CRSS depends on pillar size with an inverse power-law scaling exponent of –0.63 independent of orientation. Planar ½<110>{111} dislocations dissociate into Shockley partials whose separations range from ~3.5–4.5nm near the screw orientation to ~5–8nm near the edge, yielding a stacking fault energy of 30±5mJ/m2. Dislocations are smoothly curved without any preferred line orientation indicating no significant anisotropy in mobilities of edge and screw segments. The shear-modulus-normalized CRSS of the HEA is not exceptionally high compared to those of certain concentrated binary FCC solid solutions. Its rough magnitude calculated using the Fleischer/Labusch models corresponds to that of a hypothetical binary with the elastic constants of our HEA, solute concentrations of 20–50 at.%, and atomic size misfit of ~4%.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Peculiarities of dislocation motion in aluminum with allowance for the Peierls relief in the presence of ultrasound

The effect of ultrasound on motion of the Frenkel–Kontorova dislocations in aluminum has been studied with inclusion of the Peierls relief. A dislocation moves at a variable rate when overcoming the Peierls barrier. The dislocation mean free path is changed under action of ultrasound at various frequencies comparable to the dislocation transition time to a neighboring valley. The stress–strain dependences have been obtained for high and low strain rates. In both the cases, a disordering takes place; however, the disordering rates and characters are different. At the resonance frequency, the strain resistance decreases, the hardening stage is shortened and the disordering stage is elongated. The dependence of the coefficient of hardening on coordinate has three segments different in characters. The coefficient of hardening decreases at the resonance frequency.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Statistics of internal stress fluctuations in dislocated crystals and relevance to density-based dislocation dynamics models

A statistical analysis of internal stress fluctuations, defined as the difference between the local mean stress and stress on dislocations, is presented for deforming crystals with 3D discrete dislocation systems. Dislocation realizations are generated using dislocation dynamics simulations and the associated stress field is computed as a superposition of a regularized stress field of dislocation lines within the domain of the solution and a complementary stress field computed via a finite-element boundary value problem. The internal stress fluctuations of interest are defined by an ensemble of the difference between the stress on dislocation lines and the local mean field stress in the crystal. The latter is established in a piecewise fashion over small voxels in the crystal thus allowing the difference between the local average stress and stress on segments to be easily estimated. The results show that the Schmid stress (resolved shear stress) and Escaig stress fluctuations on various slip systems sampled over a random set of points follow a Cauchy (Lorentz) distribution at all strain levels, with the amplitude and width of the distribution being dependent on the strain. Finally, the implications of the Schmid and Escaig internal stress fluctuations are discussed from the points of view of dislocation cross-slip and the dislocation motion in continuum dislocation dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Decoding the interstitial/vacancy nature of dislocation loops with their morphological fingerprints in face-centered cubic structure

Dislocation loops are critical defects inducing detrimental effects like embrittlement and swelling in materials under irradiation. Distinguishing their nature (interstitial- or vacancy-type) is a long-standing challenge with great implications for understanding radiation damage. Here, we demonstrate that the morphology of radiation-induced Frank loops can unveil their nature in face-centered cubic (fcc) structure: Circular loops are interstitial-type in all fcc materials, while segmented loops are vacancy-type in high stacking fault energy (SFE) alloys but varied-type in low SFE and high-entropy alloys. The polygonal shape is attributed to the dissociation of an a 0 /3<111> dislocation into an a 0 /6<112> Shockley partial and an a 0 /6<110> stair-rod dislocation. The dissociation of vacancy loops is energetically favorable, whereas interstitial loops require external stimuli to promote dislocation propagation. This “morphology-nature” correlation not only highlights the asymmetry of vacancy/interstitial loops but also offers an efficient way to distinguish loop nature for a wide range of materials.

Ma, Kan [City Univ. of Hong Kong, Kowloon (Hong Ko↗
DOE OSTI2023

Automated extraction of interfacial dislocations and disconnections from atomistic data

Here we introduce Interfacial Line Defect Analysis (ILDA), a method for identifying and extracting interfacial dislocations and disconnections with little-to-no input from the user on the nature of the interface. By simply providing the orientations and coherency strains for the crystals in a coherent reference state of the interface, ILDA provides exact Burgers vectors. Alternatively, these orientations and strains can be estimated using local atomic deformation gradients, making ILDA fully automated and providing estimated Burgers vectors. ILDA also determines the step height associated with each defect line segment, in case the associated defect is a disconnection. The heart of the method is the identification of atoms residing at co-incidence sites between the two crystals and the construction of a surface mesh connecting these sites that is used to compose Burgers circuits and insert defect line segments. We demonstrate the performance of ILDA in two test cases: a twist grain boundary and a phase boundary.

36 MATERIALS SCIENCE↗